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For: Mobadersany P, Yousefi S, Amgad M, Gutman DA, Barnholtz-Sloan JS, Velázquez Vega JE, Brat DJ, Cooper LAD. Predicting cancer outcomes from histology and genomics using convolutional networks. Proc Natl Acad Sci U S A 2018;115:E2970-9. [PMID: 29531073 DOI: 10.1073/pnas.1717139115] [Cited by in Crossref: 271] [Cited by in F6Publishing: 207] [Article Influence: 67.8] [Reference Citation Analysis]
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4 Saha M, Amin SB, Sharma A, Kumar TKS, Kalia RK. AI-DRIVEN QUANTIFICATION OF GROUND GLASS OPACITIES IN LUNGS OF COVID-19 PATIENTS USING 3D COMPUTED TOMOGRAPHY IMAGING. medRxiv 2021:2021. [PMID: 34268519 DOI: 10.1101/2021.07.06.21260109] [Reference Citation Analysis]
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8 Levine AB, Peng J, Farnell D, Nursey M, Wang Y, Naso JR, Ren H, Farahani H, Chen C, Chiu D, Talhouk A, Sheffield B, Riazy M, Ip PP, Parra-Herran C, Mills A, Singh N, Tessier-Cloutier B, Salisbury T, Lee J, Salcudean T, Jones SJ, Huntsman DG, Gilks CB, Yip S, Bashashati A. Synthesis of diagnostic quality cancer pathology images by generative adversarial networks. J Pathol 2020;252:178-88. [PMID: 32686118 DOI: 10.1002/path.5509] [Cited by in Crossref: 8] [Cited by in F6Publishing: 6] [Article Influence: 4.0] [Reference Citation Analysis]
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10 Tizhoosh HR, Pantanowitz L. Artificial Intelligence and Digital Pathology: Challenges and Opportunities. J Pathol Inform 2018;9:38. [PMID: 30607305 DOI: 10.4103/jpi.jpi_53_18] [Cited by in Crossref: 113] [Cited by in F6Publishing: 87] [Article Influence: 28.3] [Reference Citation Analysis]
11 Wang Z, Li R, Wang M, Li A. GPDBN: deep bilinear network integrating both genomic data and pathological images for breast cancer prognosis prediction. Bioinformatics 2021:btab185. [PMID: 33734318 DOI: 10.1093/bioinformatics/btab185] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
12 Burgos N, Bottani S, Faouzi J, Thibeau-Sutre E, Colliot O. Deep learning for brain disorders: from data processing to disease treatment. Brief Bioinform 2021;22:1560-76. [PMID: 33316030 DOI: 10.1093/bib/bbaa310] [Reference Citation Analysis]
13 Chiu YC, Chen HH, Gorthi A, Mostavi M, Zheng S, Huang Y, Chen Y. Deep learning of pharmacogenomics resources: moving towards precision oncology. Brief Bioinform 2020;21:2066-83. [PMID: 31813953 DOI: 10.1093/bib/bbz144] [Cited by in Crossref: 15] [Cited by in F6Publishing: 12] [Article Influence: 5.0] [Reference Citation Analysis]
14 Lin H, Chen H, Weng L, Shao J, Lin J. Automatic detection of oral cancer in smartphone-based images using deep learning for early diagnosis. J Biomed Opt 2021;26. [PMID: 34453419 DOI: 10.1117/1.JBO.26.8.086007] [Reference Citation Analysis]
15 Srinidhi CL, Ciga O, Martel AL. Deep neural network models for computational histopathology: A survey. Med Image Anal 2021;67:101813. [PMID: 33049577 DOI: 10.1016/j.media.2020.101813] [Cited by in Crossref: 26] [Cited by in F6Publishing: 24] [Article Influence: 13.0] [Reference Citation Analysis]
16 Bizzego A, Gabrieli G, Neoh MJY, Esposito G. Improving the Efficacy of Deep-Learning Models for Heart Beat Detection on Heterogeneous Datasets. Bioengineering (Basel) 2021;8:193. [PMID: 34940346 DOI: 10.3390/bioengineering8120193] [Reference Citation Analysis]
17 Schneider L, Laiouar-Pedari S, Kuntz S, Krieghoff-Henning E, Hekler A, Kather JN, Gaiser T, Fröhling S, Brinker TJ. Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review. Eur J Cancer 2022;160:80-91. [PMID: 34810047 DOI: 10.1016/j.ejca.2021.10.007] [Reference Citation Analysis]
18 Chen CL, Chen CC, Yu WH, Chen SH, Chang YC, Hsu TI, Hsiao M, Yeh CY, Chen CY. An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning. Nat Commun 2021;12:1193. [PMID: 33608558 DOI: 10.1038/s41467-021-21467-y] [Cited by in Crossref: 2] [Cited by in F6Publishing: 6] [Article Influence: 2.0] [Reference Citation Analysis]
19 You S, Sun Y, Yang L, Park J, Tu H, Marjanovic M, Sinha S, Boppart SA. Real-time intraoperative diagnosis by deep neural network driven multiphoton virtual histology. NPJ Precis Oncol 2019;3:33. [PMID: 31872065 DOI: 10.1038/s41698-019-0104-3] [Cited by in Crossref: 9] [Cited by in F6Publishing: 7] [Article Influence: 3.0] [Reference Citation Analysis]
20 Sadeghzadehyazdi N, Batabyal T, Acton ST. Modeling spatiotemporal patterns of gait anomaly with a CNN-LSTM deep neural network. Expert Systems with Applications 2021;185:115582. [DOI: 10.1016/j.eswa.2021.115582] [Reference Citation Analysis]
21 Turkki R, Byckhov D, Lundin M, Isola J, Nordling S, Kovanen PE, Verrill C, von Smitten K, Joensuu H, Lundin J, Linder N. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat 2019;177:41-52. [PMID: 31119567 DOI: 10.1007/s10549-019-05281-1] [Cited by in Crossref: 27] [Cited by in F6Publishing: 19] [Article Influence: 9.0] [Reference Citation Analysis]
22 Banja J. Welcoming the "Intel-ethicist". Hastings Cent Rep 2019;49:33-6. [PMID: 30790303 DOI: 10.1002/hast.976] [Reference Citation Analysis]
23 Chen L, Zeng H, Xiang Y, Huang Y, Luo Y, Ma X. Histopathological Images and Multi-Omics Integration Predict Molecular Characteristics and Survival in Lung Adenocarcinoma. Front Cell Dev Biol 2021;9:720110. [PMID: 34708036 DOI: 10.3389/fcell.2021.720110] [Reference Citation Analysis]
24 Vale-Silva LA, Rohr K. Long-term cancer survival prediction using multimodal deep learning. Sci Rep 2021;11:13505. [PMID: 34188098 DOI: 10.1038/s41598-021-92799-4] [Reference Citation Analysis]
25 Rahman A, Jahangir C, Lynch SM, Alattar N, Aura C, Russell N, Lanigan F, Gallagher WM. Advances in tissue-based imaging: impact on oncology research and clinical practice. Expert Rev Mol Diagn 2020;20:1027-37. [PMID: 32510287 DOI: 10.1080/14737159.2020.1770599] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
26 Goecks J, Jalili V, Heiser LM, Gray JW. How Machine Learning Will Transform Biomedicine. Cell 2020;181:92-101. [PMID: 32243801 DOI: 10.1016/j.cell.2020.03.022] [Cited by in Crossref: 46] [Cited by in F6Publishing: 32] [Article Influence: 23.0] [Reference Citation Analysis]
27 Kudo Y. Predicting cancer outcome: Artificial intelligence vs. pathologists. Oral Dis 2019;25:643-5. [PMID: 30095205 DOI: 10.1111/odi.12954] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 0.8] [Reference Citation Analysis]
28 Pianykh OS, Langs G, Dewey M, Enzmann DR, Herold CJ, Schoenberg SO, Brink JA. Continuous Learning AI in Radiology: Implementation Principles and Early Applications. Radiology 2020;297:6-14. [DOI: 10.1148/radiol.2020200038] [Cited by in Crossref: 17] [Cited by in F6Publishing: 7] [Article Influence: 8.5] [Reference Citation Analysis]
29 Truong AH, Sharmanska V, Limbӓck-Stanic C, Grech-Sollars M. Optimization of deep learning methods for visualization of tumor heterogeneity and brain tumor grading through digital pathology. Neurooncol Adv 2020;2:vdaa110. [PMID: 33196039 DOI: 10.1093/noajnl/vdaa110] [Cited by in Crossref: 4] [Cited by in F6Publishing: 3] [Article Influence: 2.0] [Reference Citation Analysis]
30 Gallins P, Saghapour E, Zhou YH. Exploring the Limits of Combined Image/'omics Analysis for Non-cancer Histological Phenotypes. Front Genet 2020;11:555886. [PMID: 33193632 DOI: 10.3389/fgene.2020.555886] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
31 Pan Y, Lei X, Zhang Y. Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach. Med Res Rev 2021. [PMID: 34346083 DOI: 10.1002/med.21847] [Reference Citation Analysis]
32 Xu W, Lin J, Gao M, Chen Y, Cao J, Pu J, Huang L, Zhao J, Qian K. Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition. Adv Sci (Weinh) 2020;7:2002021. [PMID: 33173737 DOI: 10.1002/advs.202002021] [Cited by in Crossref: 17] [Cited by in F6Publishing: 11] [Article Influence: 8.5] [Reference Citation Analysis]
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34 Bianconi F, Kather JN, Reyes-Aldasoro CC. Experimental Assessment of Color Deconvolution and Color Normalization for Automated Classification of Histology Images Stained with Hematoxylin and Eosin. Cancers (Basel) 2020;12:E3337. [PMID: 33187299 DOI: 10.3390/cancers12113337] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 2.0] [Reference Citation Analysis]
35 Huang Z, Johnson TS, Han Z, Helm B, Cao S, Zhang C, Salama P, Rizkalla M, Yu CY, Cheng J, Xiang S, Zhan X, Zhang J, Huang K. Deep learning-based cancer survival prognosis from RNA-seq data: approaches and evaluations. BMC Med Genomics 2020;13:41. [PMID: 32241264 DOI: 10.1186/s12920-020-0686-1] [Cited by in Crossref: 15] [Cited by in F6Publishing: 10] [Article Influence: 7.5] [Reference Citation Analysis]
36 Chu CS, Lee NP, Ho JWK, Choi SW, Thomson PJ. Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma: A Review. JAMA Otolaryngol Head Neck Surg 2021;147:893-900. [PMID: 34410314 DOI: 10.1001/jamaoto.2021.2028] [Reference Citation Analysis]
37 Chen H, He Y, Jia W. Precise hepatectomy in the intelligent digital era. Int J Biol Sci 2020;16:365-73. [PMID: 32015674 DOI: 10.7150/ijbs.39387] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
38 Gao Y, Cui Y. Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nat Commun 2020;11:5131. [PMID: 33046699 DOI: 10.1038/s41467-020-18918-3] [Cited by in Crossref: 7] [Cited by in F6Publishing: 4] [Article Influence: 3.5] [Reference Citation Analysis]
39 Bera K, Schalper KA, Rimm DL, Velcheti V, Madabhushi A. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. 2019;16:703-715. [PMID: 31399699 DOI: 10.1038/s41571-019-0252-y] [Cited by in Crossref: 191] [Cited by in F6Publishing: 169] [Article Influence: 63.7] [Reference Citation Analysis]
40 Abels E, Pantanowitz L, Aeffner F, Zarella MD, van der Laak J, Bui MM, Vemuri VN, Parwani AV, Gibbs J, Agosto-Arroyo E, Beck AH, Kozlowski C. Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association. J Pathol 2019;249:286-94. [PMID: 31355445 DOI: 10.1002/path.5331] [Cited by in Crossref: 68] [Cited by in F6Publishing: 59] [Article Influence: 22.7] [Reference Citation Analysis]
41 Kim D, Min Y, Oh JM, Cho YK. AI-powered transmitted light microscopy for functional analysis of live cells. Sci Rep 2019;9:18428. [PMID: 31804589 DOI: 10.1038/s41598-019-54961-x] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 1.7] [Reference Citation Analysis]
42 Fu Y, Jung AW, Torne RV, Gonzalez S, Vöhringer H, Shmatko A, Yates LR, Jimenez-linan M, Moore L, Gerstung M. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat Cancer 2020;1:800-10. [DOI: 10.1038/s43018-020-0085-8] [Cited by in Crossref: 55] [Cited by in F6Publishing: 19] [Article Influence: 27.5] [Reference Citation Analysis]
43 Wu J, Liu C, Liu X, Sun W, Li L, Gao N, Zhang Y, Yang X, Zhang J, Wang H, Liu X, Huang X, Zhang Y, Cheng R, Chi K, Mao L, Zhou L, Lin D, Ling S. Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer. Mod Pathol 2021. [PMID: 34518630 DOI: 10.1038/s41379-021-00904-9] [Reference Citation Analysis]
44 Shi JY, Wang X, Ding GY, Dong Z, Han J, Guan Z, Ma LJ, Zheng Y, Zhang L, Yu GZ, Wang XY, Ding ZB, Ke AW, Yang H, Wang L, Ai L, Cao Y, Zhou J, Fan J, Liu X, Gao Q. Exploring prognostic indicators in the pathological images of hepatocellular carcinoma based on deep learning. Gut 2021;70:951-61. [PMID: 32998878 DOI: 10.1136/gutjnl-2020-320930] [Cited by in Crossref: 5] [Cited by in F6Publishing: 6] [Article Influence: 2.5] [Reference Citation Analysis]
45 Tufail AB, Ma YK, Kaabar MKA, Martínez F, Junejo AR, Ullah I, Khan R. Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. Comput Math Methods Med 2021;2021:9025470. [PMID: 34754327 DOI: 10.1155/2021/9025470] [Cited by in Crossref: 2] [Article Influence: 2.0] [Reference Citation Analysis]
46 Schulz S, Woerl AC, Jungmann F, Glasner C, Stenzel P, Strobl S, Fernandez A, Wagner DC, Haferkamp A, Mildenberger P, Roth W, Foersch S. Multimodal Deep Learning for Prognosis Prediction in Renal Cancer. Front Oncol 2021;11:788740. [PMID: 34900744 DOI: 10.3389/fonc.2021.788740] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
47 Wood-Trageser MA, Lesniak AJ, Demetris AJ. Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring. Transplantation 2019;103:1306-22. [PMID: 30768568 DOI: 10.1097/TP.0000000000002656] [Cited by in Crossref: 9] [Cited by in F6Publishing: 5] [Article Influence: 4.5] [Reference Citation Analysis]
48 Zimmerman L, Zelichov O, Aizenmann A, Barbash Z, Vidne M, Tarcic G. A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks. Sci Rep 2020;10:4192. [PMID: 32144301 DOI: 10.1038/s41598-020-61173-1] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 1.5] [Reference Citation Analysis]
49 Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16:391-403. [PMID: 31092914 DOI: 10.1038/s41585-019-0193-3] [Cited by in Crossref: 83] [Cited by in F6Publishing: 65] [Article Influence: 41.5] [Reference Citation Analysis]
50 Cary MP Jr, Zhuang F, Draelos RL, Pan W, Amarasekara S, Douthit BJ, Kang Y, Colón-Emeric CS. Machine Learning Algorithms to Predict Mortality and Allocate Palliative Care for Older Patients With Hip Fracture. J Am Med Dir Assoc 2021;22:291-6. [PMID: 33132014 DOI: 10.1016/j.jamda.2020.09.025] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
51 Su F, Sun Y, Hu Y, Yuan P, Wang X, Wang Q, Li J, Ji J. Development and validation of a deep learning system for ascites cytopathology interpretation. Gastric Cancer 2020;23:1041-50. [DOI: 10.1007/s10120-020-01093-1] [Cited by in Crossref: 6] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
52 Koo J, Choi K, Lee P, Polley A, Pudupakam RS, Tsang J, Fernandez E, Han EJ, Park S, Swartzfager D, Qi NSX, Jung M, Ocnean M, Kim HU, Lim S. Predicting Dynamic Clinical Outcomes of the Chemotherapy for Canine Lymphoma Patients Using a Machine Learning Model. Vet Sci 2021;8:301. [PMID: 34941828 DOI: 10.3390/vetsci8120301] [Reference Citation Analysis]
53 Jang HJ, Song IH, Lee SH. Deep Learning for Automatic Subclassification of Gastric Carcinoma Using Whole-Slide Histopathology Images. Cancers (Basel) 2021;13:3811. [PMID: 34359712 DOI: 10.3390/cancers13153811] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
54 Goda K. Biophotonics and beyond. APL Photonics 2019;4:050401. [DOI: 10.1063/1.5100614] [Cited by in Crossref: 3] [Article Influence: 1.0] [Reference Citation Analysis]
55 Feng S, Yu X, Liang W, Li X, Zhong W, Hu W, Zhang H, Feng Z, Song M, Zhang J, Zhang X. Development of a Deep Learning Model to Assist With Diagnosis of Hepatocellular Carcinoma. Front Oncol 2021;11:762733. [DOI: 10.3389/fonc.2021.762733] [Reference Citation Analysis]
56 Prior F, Almeida J, Kathiravelu P, Kurc T, Smith K, Fitzgerald TJ, Saltz J. Open access image repositories: high-quality data to enable machine learning research. Clin Radiol 2020;75:7-12. [PMID: 31040006 DOI: 10.1016/j.crad.2019.04.002] [Cited by in Crossref: 15] [Cited by in F6Publishing: 11] [Article Influence: 5.0] [Reference Citation Analysis]
57 Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. Harnessing non-destructive 3D pathology. Nat Biomed Eng 2021;5:203-18. [PMID: 33589781 DOI: 10.1038/s41551-020-00681-x] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
58 Shao D, Dai Y, Li N, Cao X, Zhao W, Cheng L, Rong Z, Huang L, Wang Y, Zhao J. Artificial intelligence in clinical research of cancers. Brief Bioinform 2021:bbab523. [PMID: 34929741 DOI: 10.1093/bib/bbab523] [Reference Citation Analysis]
59 Wang X, Wang D, Yao Z, Xin B, Wang B, Lan C, Qin Y, Xu S, He D, Liu Y. Machine Learning Models for Multiparametric Glioma Grading With Quantitative Result Interpretations. Front Neurosci 2018;12:1046. [PMID: 30686996 DOI: 10.3389/fnins.2018.01046] [Cited by in Crossref: 19] [Cited by in F6Publishing: 16] [Article Influence: 6.3] [Reference Citation Analysis]
60 Doğan RS, Yılmaz B. Comparison of deep learning and conventional machine learning methods for classification of colon polyp types. The EuroBiotech Journal 2021;5:34-42. [DOI: 10.2478/ebtj-2021-0006] [Reference Citation Analysis]
61 Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med. 2019;11:70. [PMID: 31744524 DOI: 10.1186/s13073-019-0689-8] [Cited by in Crossref: 45] [Cited by in F6Publishing: 34] [Article Influence: 15.0] [Reference Citation Analysis]
62 Wang KS, Yu G, Xu C, Meng XH, Zhou J, Zheng C, Deng Z, Shang L, Liu R, Su S, Zhou X, Li Q, Li J, Wang J, Ma K, Qi J, Hu Z, Tang P, Deng J, Qiu X, Li BY, Shen WD, Quan RP, Yang JT, Huang LY, Xiao Y, Yang ZC, Li Z, Wang SC, Ren H, Liang C, Guo W, Li Y, Xiao H, Gu Y, Yun JP, Huang D, Song Z, Fan X, Chen L, Yan X, Huang ZC, Huang J, Luttrell J, Zhang CY, Zhou W, Zhang K, Yi C, Wu C, Shen H, Wang YP, Xiao HM, Deng HW. Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence. BMC Med. 2021;19:76. [PMID: 33752648 DOI: 10.1186/s12916-021-01942-5] [Cited by in Crossref: 2] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]
63 Echle A, Rindtorff NT, Brinker TJ, Luedde T, Pearson AT, Kather JN. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br J Cancer 2021;124:686-96. [PMID: 33204028 DOI: 10.1038/s41416-020-01122-x] [Cited by in Crossref: 20] [Cited by in F6Publishing: 19] [Article Influence: 10.0] [Reference Citation Analysis]
64 Lvu W, Fei X, Chen C, Zhang B. In silico identification of the prognostic biomarkers and therapeutic targets associated with cancer stem cell characteristics of glioma. Biosci Rep 2020;40:BSR20201037. [PMID: 32725165 DOI: 10.1042/BSR20201037] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
65 Luo Y, Tseng HH, Cui S, Wei L, Ten Haken RK, El Naqa I. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR Open. 2019;1:20190021. [PMID: 33178948 DOI: 10.1259/bjro.20190021] [Cited by in Crossref: 11] [Cited by in F6Publishing: 9] [Article Influence: 3.7] [Reference Citation Analysis]
66 Mobadersany P, Cooper LAD, Goldstein JA. GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images. Lab Invest 2021;101:942-51. [PMID: 33674784 DOI: 10.1038/s41374-021-00579-5] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
67 Van Eycke YR, Foucart A, Decaestecker C. Strategies to Reduce the Expert Supervision Required for Deep Learning-Based Segmentation of Histopathological Images. Front Med (Lausanne) 2019;6:222. [PMID: 31681779 DOI: 10.3389/fmed.2019.00222] [Cited by in Crossref: 7] [Cited by in F6Publishing: 6] [Article Influence: 2.3] [Reference Citation Analysis]
68 Howard FM, Dolezal J, Kochanny S, Schulte J, Chen H, Heij L, Huo D, Nanda R, Olopade OI, Kather JN, Cipriani N, Grossman RL, Pearson AT. The impact of site-specific digital histology signatures on deep learning model accuracy and bias. Nat Commun 2021;12:4423. [PMID: 34285218 DOI: 10.1038/s41467-021-24698-1] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
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